{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1b7c1833-abdd-45ff-810b-96ffb7968eef",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "## 1. 数据读取"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "f10a3948-0a9f-4205-8193-da50b0724021",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1200000 entries, 0 to 1199999\n",
      "Data columns (total 16 columns):\n",
      " #   Column  Non-Null Count    Dtype  \n",
      "---  ------  --------------    -----  \n",
      " 0   A       1200000 non-null  float64\n",
      " 1   B       1200000 non-null  float64\n",
      " 2   C       1200000 non-null  float64\n",
      " 3   D       1200000 non-null  float64\n",
      " 4   E       1200000 non-null  float64\n",
      " 5   F       1200000 non-null  float64\n",
      " 6   G       1200000 non-null  float64\n",
      " 7   H       1200000 non-null  float64\n",
      " 8   I       1200000 non-null  float64\n",
      " 9   J       1200000 non-null  float64\n",
      " 10  K       1200000 non-null  float64\n",
      " 11  L       1200000 non-null  float64\n",
      " 12  M       1200000 non-null  float64\n",
      " 13  N       1200000 non-null  float64\n",
      " 14  O       1200000 non-null  float64\n",
      " 15  Class   1200000 non-null  int64  \n",
      "dtypes: float64(15), int64(1)\n",
      "memory usage: 146.5 MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.model_selection import train_test_split\n",
    "from skl2onnx import convert_sklearn\n",
    "from skl2onnx.common.data_types import FloatTensorType\n",
    "import onnxruntime as rt\n",
    "import numpy as np\n",
    "\n",
    "from sklearn2pmml.decoration import ContinuousDomain\n",
    "from sklearn2pmml.pipeline import PMMLPipeline\n",
    "\n",
    "from sklearn2pmml import sklearn2pmml\n",
    "\n",
    "# 读取CSV文件\n",
    "csv_file_path = 'data_public.csv'\n",
    "data = pd.read_csv(csv_file_path)\n",
    "\n",
    "# 打印数据的基本信息\n",
    "print(data.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "165d7d7c-d9d1-46db-9a3a-d904e378a7f7",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据集的行数和列数: (1200000, 16)\n",
      "数据类型:\n",
      "A        float64\n",
      "B        float64\n",
      "C        float64\n",
      "D        float64\n",
      "E        float64\n",
      "F        float64\n",
      "G        float64\n",
      "H        float64\n",
      "I        float64\n",
      "J        float64\n",
      "K        float64\n",
      "L        float64\n",
      "M        float64\n",
      "N        float64\n",
      "O        float64\n",
      "Class      int64\n",
      "dtype: object\n",
      "\n",
      "每列的缺失值数量:\n",
      "A        0\n",
      "B        0\n",
      "C        0\n",
      "D        0\n",
      "E        0\n",
      "F        0\n",
      "G        0\n",
      "H        0\n",
      "I        0\n",
      "J        0\n",
      "K        0\n",
      "L        0\n",
      "M        0\n",
      "N        0\n",
      "O        0\n",
      "Class    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# 检查数据的基本信息\n",
    "print(f\"数据集的行数和列数: {data.shape}\")\n",
    "print(\"数据类型:\")\n",
    "print(data.dtypes)\n",
    "\n",
    "# 检查缺失值\n",
    "missing_values = data.isnull().sum()\n",
    "print(\"\\n每列的缺失值数量:\")\n",
    "print(missing_values)\n",
    "\n",
    "# 数据集的特征列\n",
    "features = data.columns[:-1]\n",
    "scaler = StandardScaler()\n",
    "data[features] = scaler.fit_transform(data[features])\n",
    "\n",
    "# 数据集的target列\n",
    "target = data.columns[data.shape[1]-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f3ce334-f07a-4c5d-b871-cad66f7ca4f2",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "## 2. 数据集划分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "403b55b7-bb5c-4542-ac37-00230bb4c1cb",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 目标变量的列名为'Class'\n",
    "target_column = 'Class'\n",
    "\n",
    "# 分离特征和目标变量\n",
    "X = data.drop(columns=target_column)\n",
    "y = data[target_column]\n",
    "\n",
    "# 标准化特征\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "\n",
    "# 划分训练集和测试集\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.3, random_state=42)\n",
    "\n",
    "# 数据采样（例如采样10%的数据）\n",
    "#sampled_data = data.sample(frac=0.1, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b8591bd-9209-401e-aa1b-5a2b979a1db3",
   "metadata": {},
   "source": [
    "## 3. 特征分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "c26812f5-e39d-452f-b0c0-b0fe60c20ed7",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "特征之间的皮尔逊相关系数:\n",
      "          A         B         C         D         E         F         G  \\\n",
      "A  1.000000  0.455949  0.991999  0.071330  0.990703  0.905353  0.972223   \n",
      "B  0.455949  1.000000  0.541742  0.865856  0.352946  0.760708  0.620607   \n",
      "C  0.991999  0.541742  1.000000  0.176224  0.971805  0.943482  0.988351   \n",
      "D  0.071330  0.865856  0.176224  1.000000 -0.047459  0.477183  0.279248   \n",
      "E  0.990703  0.352946  0.971805 -0.047459  1.000000  0.849129  0.939705   \n",
      "F  0.905353  0.760708  0.943482  0.477183  0.849129  1.000000  0.969055   \n",
      "G  0.972223  0.620607  0.988351  0.279248  0.939705  0.969055  1.000000   \n",
      "H  0.988807  0.339549  0.968342 -0.062451  0.997116  0.841227  0.934714   \n",
      "I  0.818399 -0.098558  0.753474 -0.502643  0.879142  0.508345  0.678043   \n",
      "J  0.870016  0.803246  0.915784  0.544357  0.805749  0.989868  0.949429   \n",
      "K  0.968827  0.246429  0.937868 -0.163679  0.989217  0.781534  0.894114   \n",
      "L  0.139619  0.854635  0.238723  0.949485  0.026319  0.518117  0.335039   \n",
      "M  0.958931  0.345030  0.941040 -0.042057  0.964769  0.823551  0.910385   \n",
      "N  0.953081  0.194578  0.916578 -0.217856  0.979925  0.745156  0.867546   \n",
      "O  0.920322  0.098805  0.873800 -0.316241  0.958885  0.675416  0.815281   \n",
      "\n",
      "          H         I         J         K         L         M         N  \\\n",
      "A  0.988807  0.818399  0.870016  0.968827  0.139619  0.958931  0.953081   \n",
      "B  0.339549 -0.098558  0.803246  0.246429  0.854635  0.345030  0.194578   \n",
      "C  0.968342  0.753474  0.915784  0.937868  0.238723  0.941040  0.916578   \n",
      "D -0.062451 -0.502643  0.544357 -0.163679  0.949485 -0.042057 -0.217856   \n",
      "E  0.997116  0.879142  0.805749  0.989217  0.026319  0.964769  0.979925   \n",
      "F  0.841227  0.508345  0.989868  0.781534  0.518117  0.823551  0.745156   \n",
      "G  0.934714  0.678043  0.949429  0.894114  0.335039  0.910385  0.867546   \n",
      "H  1.000000  0.886017  0.796856  0.990875  0.012005  0.964627  0.982403   \n",
      "I  0.886017  1.000000  0.439881  0.926217 -0.418110  0.848801  0.943365   \n",
      "J  0.796856  0.439881  1.000000  0.730841  0.579309  0.781815  0.691273   \n",
      "K  0.990875  0.926217  0.730841  1.000000 -0.085543  0.956598  0.992158   \n",
      "L  0.012005 -0.418110  0.579309 -0.085543  1.000000  0.029013 -0.138097   \n",
      "M  0.964627  0.848801  0.781815  0.956598  0.029013  1.000000  0.947381   \n",
      "N  0.982403  0.943365  0.691273  0.992158 -0.138097  0.947381  1.000000   \n",
      "O  0.962873  0.970965  0.615931  0.982980 -0.233820  0.926620  0.988920   \n",
      "\n",
      "          O  \n",
      "A  0.920322  \n",
      "B  0.098805  \n",
      "C  0.873800  \n",
      "D -0.316241  \n",
      "E  0.958885  \n",
      "F  0.675416  \n",
      "G  0.815281  \n",
      "H  0.962873  \n",
      "I  0.970965  \n",
      "J  0.615931  \n",
      "K  0.982980  \n",
      "L -0.233820  \n",
      "M  0.926620  \n",
      "N  0.988920  \n",
      "O  1.000000  \n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# 计算特征之间的皮尔逊相关系数\n",
    "correlation_matrix = X.corr()\n",
    "\n",
    "# 打印相关系数矩阵\n",
    "print(\"特征之间的皮尔逊相关系数:\")\n",
    "print(correlation_matrix)\n",
    "\n",
    "# 可视化相关系数矩阵\n",
    "plt.figure(figsize=(12, 10))\n",
    "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', linewidths=0.5)\n",
    "plt.title('Feature Correlation Matrix (Pearson)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "f1bf7b82-615d-4048-95c6-19d52fae5896",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "解释方差比率:\n",
      "[7.33864677e-01 2.49030223e-01 5.94772600e-03 4.07704155e-03\n",
      " 3.46584323e-03 6.57804933e-04 5.61838589e-04 4.71665772e-04\n",
      " 4.61722639e-04 3.82898239e-04 3.11013079e-04 2.56592626e-04\n",
      " 2.01142386e-04 1.84748847e-04 1.25062088e-04]\n",
      "累计解释方差比率:\n",
      "[0.73386468 0.9828949  0.98884263 0.99291967 0.99638551 0.99704332\n",
      " 0.99760515 0.99807682 0.99853854 0.99892144 0.99923245 0.99948905\n",
      " 0.99969019 0.99987494 1.        ]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "# 进行PCA分析\n",
    "pca = PCA()\n",
    "X_pca = pca.fit_transform(X_scaled)\n",
    "\n",
    "\n",
    "# 解释方差比率\n",
    "explained_variance_ratio = pca.explained_variance_ratio_\n",
    "\n",
    "# 累计解释方差比率\n",
    "cumulative_explained_variance_ratio = explained_variance_ratio.cumsum()\n",
    "\n",
    "# 打印解释方差比率和累计解释方差比率\n",
    "print(\"解释方差比率:\")\n",
    "print(explained_variance_ratio)\n",
    "print(\"累计解释方差比率:\")\n",
    "print(cumulative_explained_variance_ratio)\n",
    "\n",
    "# 可视化累计解释方差比率\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(range(1, len(cumulative_explained_variance_ratio) + 1), cumulative_explained_variance_ratio, marker='o', linestyle='--')\n",
    "plt.xlabel('Number of Components')\n",
    "plt.ylabel('Cumulative Explained Variance')\n",
    "plt.title('Explained Variance by Principal Components')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f775df5-2ae7-429f-af11-a06ee997b1ac",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "从这些数值可以看出，前两个主成分解释了绝大部分的方差（分别为73.39%和24.90%）。前两个主成分累计解释了98.29%的方差。过PCA降维，可以显著减少特征数量，同时保留数据中的主要信息，有助于提高后续模型的性能和训练效率。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "6aa3524b-1fa9-4c42-8944-d602adb76b6c",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 可视化前两个主成分\n",
    "pca = PCA(n_components=2)\n",
    "X_pca = pca.fit_transform(X_scaled)\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.scatter(X_pca[:, 0], X_pca[:, 1], c=y, cmap='viridis', edgecolor='k', s=50)\n",
    "plt.xlabel('Principal Component 1')\n",
    "plt.ylabel('Principal Component 2')\n",
    "plt.title('PCA of Dataset')\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2f7546c-9400-4298-8d50-ccb9c0b1a953",
   "metadata": {},
   "source": [
    "另外，使用随机森林模型计算特征重要性，进行佐证"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "1a657da6-0092-40bb-b34c-79f8ae1fc422",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "特征重要性:\n",
      "   Feature  Importance\n",
      "10       K    0.111885\n",
      "8        I    0.098191\n",
      "13       N    0.095236\n",
      "14       O    0.083292\n",
      "3        D    0.082971\n",
      "4        E    0.075793\n",
      "7        H    0.071223\n",
      "12       M    0.052810\n",
      "6        G    0.049707\n",
      "2        C    0.049253\n",
      "11       L    0.048993\n",
      "9        J    0.047207\n",
      "0        A    0.047013\n",
      "1        B    0.043482\n",
      "5        F    0.042943\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 36.3 s, sys: 293 ms, total: 36.6 s\n",
      "Wall time: 36 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "import numpy as np\n",
    "\n",
    "# 合并X和y\n",
    "data_combined = pd.concat([pd.DataFrame(X_scaled, columns=X.columns), y.reset_index(drop=True)], axis=1)\n",
    "\n",
    "# 进行0.1的采样\n",
    "data_sampled = data_combined.sample(frac=0.1, random_state=42)\n",
    "\n",
    "# 再次分离特征和目标变量\n",
    "X_sampled = data_sampled.drop(columns=target_column)\n",
    "y_sampled = data_sampled[target_column]\n",
    "\n",
    "# 划分训练集和测试集\n",
    "X_train_sample, X_test_sample, y_train_sample, y_test_sample = train_test_split(X_sampled, y_sampled, test_size=0.3, random_state=42)\n",
    "\n",
    "# 训练随机森林模型\n",
    "rf_model = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "rf_model.fit(X_train_sample, y_train_sample)\n",
    "\n",
    "# 计算特征重要性\n",
    "feature_importances = rf_model.feature_importances_\n",
    "importance_df = pd.DataFrame({'Feature': X.columns, 'Importance': feature_importances})\n",
    "importance_df = importance_df.sort_values(by='Importance', ascending=False)\n",
    "\n",
    "# 打印特征重要性\n",
    "print(\"特征重要性:\")\n",
    "print(importance_df)\n",
    "\n",
    "# 可视化特征重要性\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.barplot(x='Importance', y='Feature', data=importance_df)\n",
    "plt.title('Feature Importance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3dca5983-4a26-47f4-9164-888db5f8521a",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    " 使用K-means聚类分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a707250e-97cf-4392-89ca-0109c8c4a329",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score\n",
    "\n",
    "# 轮廓系数\n",
    "silhouette_scores = []\n",
    "k_values = range(2, 5)  # 修改k值范围与计算的轮廓系数匹配\n",
    "for k in k_values:\n",
    "    kmeans = KMeans(n_clusters=k, random_state=0).fit(X_scaled)\n",
    "    silhouette_scores.append(silhouette_score(X_scaled, kmeans.labels_))\n",
    "\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(k_values, silhouette_scores, marker='o')\n",
    "plt.xlabel('Number of clusters')\n",
    "plt.ylabel('Silhouette Score')\n",
    "plt.title('Silhouette Method')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b037c1b5-b199-48a9-91b9-ac5c25114a2a",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# K-means聚类\n",
    "kmeans = KMeans(n_clusters=3, random_state=42)\n",
    "%timeit kmeans_labels = kmeans.fit_predict(X_scaled)\n",
    "\n",
    "# 可视化K-means聚类结果\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.scatter(X_scaled[:, 0], X_scaled[:, 1], c=kmeans_labels, cmap='viridis', edgecolor='k', s=50)\n",
    "plt.xlabel('Feature 1')\n",
    "plt.ylabel('Feature 2')\n",
    "plt.title('K-means Clustering')\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "09e4a957-5fda-45ca-a825-3db61094abca",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import MiniBatchKMeans\n",
    "\n",
    "# 使用 MiniBatch K-means 进行聚类\n",
    "minibatch_kmeans = MiniBatchKMeans(n_clusters=3, batch_size=10000, random_state=42)\n",
    "%timeit minibatch_kmeans_labels = minibatch_kmeans.fit_predict(X_scaled)\n",
    "\n",
    "# 可视化 MiniBatch K-means 聚类结果\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.scatter(X_scaled[:, 0], X_scaled[:, 1], c=minibatch_kmeans_labels, cmap='viridis', edgecolor='k', s=50)\n",
    "plt.xlabel('Feature 1')\n",
    "plt.ylabel('Feature 2')\n",
    "plt.title('MiniBatch K-means Clustering')\n",
    "plt.colorbar()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c806012e-cf2d-476d-818f-1f1158c0bf36",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.neighbors import NearestNeighbors\n",
    "from sklearn.cluster import DBSCAN\n",
    "\n",
    "# 使用近似最近邻搜索加速DBSCAN\n",
    "nn = NearestNeighbors(n_neighbors=5, algorithm='auto').fit(X_scaled)\n",
    "distances, indices = nn.kneighbors(X_scaled)\n",
    "eps = np.percentile(distances[:, 4], 95)\n",
    "\n",
    "dbscan = DBSCAN(eps=eps, min_samples=5, n_jobs=-1)  # 使用所有可用的CPU核\n",
    "dbscan_labels = dbscan.fit_predict(X_scaled)\n",
    "\n",
    "# 可视化DBSCAN聚类结果\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.scatter(X_scaled[:, 0], X_scaled[:, 1], c=dbscan_labels, cmap='viridis', edgecolor='k', s=50)\n",
    "plt.xlabel('Feature 1')\n",
    "plt.ylabel('Feature 2')\n",
    "plt.title('DBSCAN Clustering with Approximate Nearest Neighbors')\n",
    "plt.colorbar()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf500bc8-cfc8-4e6a-a261-e099ff880f87",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "## 4. 模型训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "5da0f47e-0322-4db2-bec7-21db2dad4778",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集评估:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       1.00      1.00      1.00         7\n",
      "           2       1.00      1.00      1.00        29\n",
      "           3       1.00      1.00      1.00        33\n",
      "\n",
      "    accuracy                           1.00        69\n",
      "   macro avg       1.00      1.00      1.00        69\n",
      "weighted avg       1.00      1.00      1.00        69\n",
      "\n",
      "测试集评估:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.20      0.50      0.29         2\n",
      "           2       1.00      0.79      0.88        14\n",
      "           3       0.71      0.71      0.71        14\n",
      "\n",
      "    accuracy                           0.73        30\n",
      "   macro avg       0.64      0.67      0.63        30\n",
      "weighted avg       0.81      0.73      0.76        30\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "# 定义数据预处理的Pipeline\n",
    "preprocessor = Pipeline(steps=[\n",
    "    ('imputer', SimpleImputer(strategy='mean')),\n",
    "    ('scaler', StandardScaler())\n",
    "])\n",
    "\n",
    "# 创建一个ColumnTransformer来应用于所有特征列\n",
    "column_transformer = ColumnTransformer(transformers=[\n",
    "    ('num', preprocessor, X.columns)\n",
    "])\n",
    "\n",
    "# 定义模型Pipeline\n",
    "classifier = RandomForestClassifier()\n",
    "pipeline = Pipeline([\n",
    "    ('preprocessor', column_transformer),\n",
    "    ('classifier', classifier)\n",
    "])\n",
    "\n",
    "# 训练模型\n",
    "pipeline.fit(X_train, y_train)\n",
    "\n",
    "# 在训练集上评估模型\n",
    "print(\"训练集评估:\")\n",
    "print(classification_report(pipeline.predict(X_train), y_train))\n",
    "\n",
    "# 在测试集上评估模型\n",
    "print(\"测试集评估:\")\n",
    "print(classification_report(pipeline.predict(X_test), y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0c8eedd-c6fc-4271-8d32-02e51f684c88",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "## 4. 模型保存和转换为ONNX格式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "4784ba2c-2908-4661-a3ac-b603576cb759",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import onnx\n",
    "from skl2onnx import convert_sklearn\n",
    "from skl2onnx.common.data_types import FloatTensorType\n",
    "\n",
    "# 定义ONNX输入类型\n",
    "input_types = [(x, FloatTensorType([None, 1])) for x in X_train.columns]\n",
    "\n",
    "try:\n",
    "    model_onnx = convert_sklearn(pipeline, initial_types=input_types)\n",
    "except Exception as e:\n",
    "    print(e)\n",
    "\n",
    "# 保存ONNX模型\n",
    "onnx_model_path = \"pipeline_model.onnx\"\n",
    "with open(onnx_model_path, \"wb\") as f:\n",
    "    f.write(model_onnx.SerializeToString())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e2f6f6e-a785-4a6c-85be-602778c184c0",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "## 5. 使用ONNX模型进行推理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "60123bd9-5fe7-4c15-8a9e-70f932455a11",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ONNX模型预测结果: [3 2 2 3 3 2 3 3 3 3 2 3 3 2 1 3 3 2 3 2 2 2 2 2 2 3 1 2 2 3]\n",
      "\n",
      "ONNX模型预测结果评估:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.50      0.20      0.29         5\n",
      "           2       0.79      1.00      0.88        11\n",
      "           3       0.71      0.71      0.71        14\n",
      "\n",
      "    accuracy                           0.73        30\n",
      "   macro avg       0.67      0.64      0.63        30\n",
      "weighted avg       0.70      0.73      0.70        30\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 加载ONNX模型并进行预测\n",
    "inputs_onnx = {k: np.array(v).astype(np.float32).reshape(-1, 1) for k, v in X_test.to_dict(orient='list').items()}\n",
    "session_onnx = rt.InferenceSession(onnx_model_path)\n",
    "predict_onnx = session_onnx.run(None, inputs_onnx)\n",
    "print(\"ONNX模型预测结果:\", predict_onnx[0])\n",
    "\n",
    "# 将ONNX预测结果与实际测试集标签进行比较\n",
    "print(\"\\nONNX模型预测结果评估:\")\n",
    "print(classification_report(y_test, predict_onnx[0]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "02176ae0-1356-42d5-85d8-8d71f24099ea",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
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       "<!-- Scaler/Scaler (op#2)\\n input0 variable\\n output0 variable1&#45;&gt;variable10 -->\n",
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       "<g id=\"node25\" class=\"node\">\n",
       "<title>Cast/Cast (op#4)\\n input0 label\\n output0 output_label</title>\n",
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       "<title>ZipMap/ZipMap (op#5)\\n input0 probabilities\\n output0 output_probability&#45;&gt;output_probability0</title>\n",
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       "</svg>\n"
      ],
      "text/plain": [
       "<graphviz.sources.Source at 0x29a0c2b10>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from onnx.tools.net_drawer import GetPydotGraph, GetOpNodeProducer\n",
    "\n",
    "import graphviz\n",
    "\n",
    "pydot_graph = GetPydotGraph(model_onnx.graph,\n",
    "                            name=model_onnx.graph.name,\n",
    "                            rankdir=\"TB\",\n",
    "                            node_producer=GetOpNodeProducer(\"docstring\",\n",
    "                                                            color=\"yellow\",\n",
    "                                                            fillcolor=\"yellow\",\n",
    "                                                            style=\"filled\"))\n",
    "\n",
    "graphviz.Source(pydot_graph.to_string())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "024149d1-77d2-4d2f-a827-18688568ceb0",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[84], line 13\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[38;5;66;03m# 使用GridSearchCV进行超参数调优\u001b[39;00m\n\u001b[1;32m     12\u001b[0m grid_search \u001b[38;5;241m=\u001b[39m GridSearchCV(estimator\u001b[38;5;241m=\u001b[39mpipeline, param_grid\u001b[38;5;241m=\u001b[39mparam_grid, cv\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5\u001b[39m, n_jobs\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, scoring\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124maccuracy\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m---> 13\u001b[0m \u001b[43mgrid_search\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     15\u001b[0m \u001b[38;5;66;03m# 输出最佳参数和模型评估\u001b[39;00m\n\u001b[1;32m     16\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m最佳参数: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mgrid_search\u001b[38;5;241m.\u001b[39mbest_params_\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[0;32m~/miniconda3/lib/python3.11/site-packages/sklearn/base.py:1473\u001b[0m, in \u001b[0;36m_fit_context.<locals>.decorator.<locals>.wrapper\u001b[0;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1466\u001b[0m     estimator\u001b[38;5;241m.\u001b[39m_validate_params()\n\u001b[1;32m   1468\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[1;32m   1469\u001b[0m     skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[1;32m   1470\u001b[0m         prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[1;32m   1471\u001b[0m     )\n\u001b[1;32m   1472\u001b[0m ):\n\u001b[0;32m-> 1473\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfit_method\u001b[49m\u001b[43m(\u001b[49m\u001b[43mestimator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniconda3/lib/python3.11/site-packages/sklearn/model_selection/_search.py:1018\u001b[0m, in \u001b[0;36mfit\u001b[0;34m(self, X, y, **params)\u001b[0m\n\u001b[1;32m   1016\u001b[0m if isinstance(scorers, _MultimetricScorer):\n\u001b[1;32m   1017\u001b[0m     self.scorer_ = scorers._scorers\n\u001b[0;32m-> 1018\u001b[0m else:\n\u001b[1;32m   1019\u001b[0m     self.scorer_ = scorers\n\u001b[1;32m   1021\u001b[0m self.cv_results_ = results\n",
      "File \u001b[0;32m~/miniconda3/lib/python3.11/site-packages/sklearn/model_selection/_search.py:1572\u001b[0m, in \u001b[0;36m_run_search\u001b[0;34m(self, evaluate_candidates)\u001b[0m\n\u001b[1;32m   1546\u001b[0m \u001b[38;5;28;01mclass\u001b[39;00m \u001b[38;5;21;01mRandomizedSearchCV\u001b[39;00m(BaseSearchCV):\n\u001b[1;32m   1547\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Randomized search on hyper parameters.\u001b[39;00m\n\u001b[1;32m   1548\u001b[0m \n\u001b[1;32m   1549\u001b[0m \u001b[38;5;124;03m    RandomizedSearchCV implements a \"fit\" and a \"score\" method.\u001b[39;00m\n\u001b[1;32m   1550\u001b[0m \u001b[38;5;124;03m    It also implements \"score_samples\", \"predict\", \"predict_proba\",\u001b[39;00m\n\u001b[1;32m   1551\u001b[0m \u001b[38;5;124;03m    \"decision_function\", \"transform\" and \"inverse_transform\" if they are\u001b[39;00m\n\u001b[1;32m   1552\u001b[0m \u001b[38;5;124;03m    implemented in the estimator used.\u001b[39;00m\n\u001b[1;32m   1553\u001b[0m \n\u001b[1;32m   1554\u001b[0m \u001b[38;5;124;03m    The parameters of the estimator used to apply these methods are optimized\u001b[39;00m\n\u001b[1;32m   1555\u001b[0m \u001b[38;5;124;03m    by cross-validated search over parameter settings.\u001b[39;00m\n\u001b[1;32m   1556\u001b[0m \n\u001b[1;32m   1557\u001b[0m \u001b[38;5;124;03m    In contrast to GridSearchCV, not all parameter values are tried out, but\u001b[39;00m\n\u001b[1;32m   1558\u001b[0m \u001b[38;5;124;03m    rather a fixed number of parameter settings is sampled from the specified\u001b[39;00m\n\u001b[1;32m   1559\u001b[0m \u001b[38;5;124;03m    distributions. The number of parameter settings that are tried is\u001b[39;00m\n\u001b[1;32m   1560\u001b[0m \u001b[38;5;124;03m    given by n_iter.\u001b[39;00m\n\u001b[1;32m   1561\u001b[0m \n\u001b[1;32m   1562\u001b[0m \u001b[38;5;124;03m    If all parameters are presented as a list,\u001b[39;00m\n\u001b[1;32m   1563\u001b[0m \u001b[38;5;124;03m    sampling without replacement is performed. If at least one parameter\u001b[39;00m\n\u001b[1;32m   1564\u001b[0m \u001b[38;5;124;03m    is given as a distribution, sampling with replacement is used.\u001b[39;00m\n\u001b[1;32m   1565\u001b[0m \u001b[38;5;124;03m    It is highly recommended to use continuous distributions for continuous\u001b[39;00m\n\u001b[1;32m   1566\u001b[0m \u001b[38;5;124;03m    parameters.\u001b[39;00m\n\u001b[1;32m   1567\u001b[0m \n\u001b[1;32m   1568\u001b[0m \u001b[38;5;124;03m    Read more in the :ref:`User Guide <randomized_parameter_search>`.\u001b[39;00m\n\u001b[1;32m   1569\u001b[0m \n\u001b[1;32m   1570\u001b[0m \u001b[38;5;124;03m    .. versionadded:: 0.14\u001b[39;00m\n\u001b[1;32m   1571\u001b[0m \n\u001b[0;32m-> 1572\u001b[0m \u001b[38;5;124;03m    Parameters\u001b[39;00m\n\u001b[1;32m   1573\u001b[0m \u001b[38;5;124;03m    ----------\u001b[39;00m\n\u001b[1;32m   1574\u001b[0m \u001b[38;5;124;03m    estimator : estimator object\u001b[39;00m\n\u001b[1;32m   1575\u001b[0m \u001b[38;5;124;03m        An object of that type is instantiated for each grid point.\u001b[39;00m\n\u001b[1;32m   1576\u001b[0m \u001b[38;5;124;03m        This is assumed to implement the scikit-learn estimator interface.\u001b[39;00m\n\u001b[1;32m   1577\u001b[0m \u001b[38;5;124;03m        Either estimator needs to provide a ``score`` function,\u001b[39;00m\n\u001b[1;32m   1578\u001b[0m \u001b[38;5;124;03m        or ``scoring`` must be passed.\u001b[39;00m\n\u001b[1;32m   1579\u001b[0m \n\u001b[1;32m   1580\u001b[0m \u001b[38;5;124;03m    param_distributions : dict or list of dicts\u001b[39;00m\n\u001b[1;32m   1581\u001b[0m \u001b[38;5;124;03m        Dictionary with parameters names (`str`) as keys and distributions\u001b[39;00m\n\u001b[1;32m   1582\u001b[0m \u001b[38;5;124;03m        or lists of parameters to try. Distributions must provide a ``rvs``\u001b[39;00m\n\u001b[1;32m   1583\u001b[0m \u001b[38;5;124;03m        method for sampling (such as those from scipy.stats.distributions).\u001b[39;00m\n\u001b[1;32m   1584\u001b[0m \u001b[38;5;124;03m        If a list is given, it is sampled uniformly.\u001b[39;00m\n\u001b[1;32m   1585\u001b[0m \u001b[38;5;124;03m        If a list of dicts is given, first a dict is sampled uniformly, and\u001b[39;00m\n\u001b[1;32m   1586\u001b[0m \u001b[38;5;124;03m        then a parameter is sampled using that dict as above.\u001b[39;00m\n\u001b[1;32m   1587\u001b[0m \n\u001b[1;32m   1588\u001b[0m \u001b[38;5;124;03m    n_iter : int, default=10\u001b[39;00m\n\u001b[1;32m   1589\u001b[0m \u001b[38;5;124;03m        Number of parameter settings that are sampled. n_iter trades\u001b[39;00m\n\u001b[1;32m   1590\u001b[0m \u001b[38;5;124;03m        off runtime vs quality of the solution.\u001b[39;00m\n\u001b[1;32m   1591\u001b[0m \n\u001b[1;32m   1592\u001b[0m \u001b[38;5;124;03m    scoring : str, callable, list, tuple or dict, default=None\u001b[39;00m\n\u001b[1;32m   1593\u001b[0m \u001b[38;5;124;03m        Strategy to evaluate the performance of the cross-validated model on\u001b[39;00m\n\u001b[1;32m   1594\u001b[0m \u001b[38;5;124;03m        the test set.\u001b[39;00m\n\u001b[1;32m   1595\u001b[0m \n\u001b[1;32m   1596\u001b[0m \u001b[38;5;124;03m        If `scoring` represents a single score, one can use:\u001b[39;00m\n\u001b[1;32m   1597\u001b[0m \n\u001b[1;32m   1598\u001b[0m \u001b[38;5;124;03m        - a single string (see :ref:`scoring_parameter`);\u001b[39;00m\n\u001b[1;32m   1599\u001b[0m \u001b[38;5;124;03m        - a callable (see :ref:`scoring`) that returns a single value.\u001b[39;00m\n\u001b[1;32m   1600\u001b[0m \n\u001b[1;32m   1601\u001b[0m \u001b[38;5;124;03m        If `scoring` represents multiple scores, one can use:\u001b[39;00m\n\u001b[1;32m   1602\u001b[0m \n\u001b[1;32m   1603\u001b[0m \u001b[38;5;124;03m        - a list or tuple of unique strings;\u001b[39;00m\n\u001b[1;32m   1604\u001b[0m \u001b[38;5;124;03m        - a callable returning a dictionary where the keys are the metric\u001b[39;00m\n\u001b[1;32m   1605\u001b[0m \u001b[38;5;124;03m          names and the values are the metric scores;\u001b[39;00m\n\u001b[1;32m   1606\u001b[0m \u001b[38;5;124;03m        - a dictionary with metric names as keys and callables a values.\u001b[39;00m\n\u001b[1;32m   1607\u001b[0m \n\u001b[1;32m   1608\u001b[0m \u001b[38;5;124;03m        See :ref:`multimetric_grid_search` for an example.\u001b[39;00m\n\u001b[1;32m   1609\u001b[0m \n\u001b[1;32m   1610\u001b[0m \u001b[38;5;124;03m        If None, the estimator's score method is used.\u001b[39;00m\n\u001b[1;32m   1611\u001b[0m \n\u001b[1;32m   1612\u001b[0m \u001b[38;5;124;03m    n_jobs : int, default=None\u001b[39;00m\n\u001b[1;32m   1613\u001b[0m \u001b[38;5;124;03m        Number of jobs to run in parallel.\u001b[39;00m\n\u001b[1;32m   1614\u001b[0m \u001b[38;5;124;03m        ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.\u001b[39;00m\n\u001b[1;32m   1615\u001b[0m \u001b[38;5;124;03m        ``-1`` means using all processors. See :term:`Glossary <n_jobs>`\u001b[39;00m\n\u001b[1;32m   1616\u001b[0m \u001b[38;5;124;03m        for more details.\u001b[39;00m\n\u001b[1;32m   1617\u001b[0m \n\u001b[1;32m   1618\u001b[0m \u001b[38;5;124;03m        .. versionchanged:: v0.20\u001b[39;00m\n\u001b[1;32m   1619\u001b[0m \u001b[38;5;124;03m           `n_jobs` default changed from 1 to None\u001b[39;00m\n\u001b[1;32m   1620\u001b[0m \n\u001b[1;32m   1621\u001b[0m \u001b[38;5;124;03m    refit : bool, str, or callable, default=True\u001b[39;00m\n\u001b[1;32m   1622\u001b[0m \u001b[38;5;124;03m        Refit an estimator using the best found parameters on the whole\u001b[39;00m\n\u001b[1;32m   1623\u001b[0m \u001b[38;5;124;03m        dataset.\u001b[39;00m\n\u001b[1;32m   1624\u001b[0m \n\u001b[1;32m   1625\u001b[0m \u001b[38;5;124;03m        For multiple metric evaluation, this needs to be a `str` denoting the\u001b[39;00m\n\u001b[1;32m   1626\u001b[0m \u001b[38;5;124;03m        scorer that would be used to find the best parameters for refitting\u001b[39;00m\n\u001b[1;32m   1627\u001b[0m \u001b[38;5;124;03m        the estimator at the end.\u001b[39;00m\n\u001b[1;32m   1628\u001b[0m \n\u001b[1;32m   1629\u001b[0m \u001b[38;5;124;03m        Where there are considerations other than maximum score in\u001b[39;00m\n\u001b[1;32m   1630\u001b[0m \u001b[38;5;124;03m        choosing a best estimator, ``refit`` can be set to a function which\u001b[39;00m\n\u001b[1;32m   1631\u001b[0m \u001b[38;5;124;03m        returns the selected ``best_index_`` given the ``cv_results``. In that\u001b[39;00m\n\u001b[1;32m   1632\u001b[0m \u001b[38;5;124;03m        case, the ``best_estimator_`` and ``best_params_`` will be set\u001b[39;00m\n\u001b[1;32m   1633\u001b[0m \u001b[38;5;124;03m        according to the returned ``best_index_`` while the ``best_score_``\u001b[39;00m\n\u001b[1;32m   1634\u001b[0m \u001b[38;5;124;03m        attribute will not be available.\u001b[39;00m\n\u001b[1;32m   1635\u001b[0m \n\u001b[1;32m   1636\u001b[0m \u001b[38;5;124;03m        The refitted estimator is made available at the ``best_estimator_``\u001b[39;00m\n\u001b[1;32m   1637\u001b[0m \u001b[38;5;124;03m        attribute and permits using ``predict`` directly on this\u001b[39;00m\n\u001b[1;32m   1638\u001b[0m \u001b[38;5;124;03m        ``RandomizedSearchCV`` instance.\u001b[39;00m\n\u001b[1;32m   1639\u001b[0m \n\u001b[1;32m   1640\u001b[0m \u001b[38;5;124;03m        Also for multiple metric evaluation, the attributes ``best_index_``,\u001b[39;00m\n\u001b[1;32m   1641\u001b[0m \u001b[38;5;124;03m        ``best_score_`` and ``best_params_`` will only be available if\u001b[39;00m\n\u001b[1;32m   1642\u001b[0m \u001b[38;5;124;03m        ``refit`` is set and all of them will be determined w.r.t this specific\u001b[39;00m\n\u001b[1;32m   1643\u001b[0m \u001b[38;5;124;03m        scorer.\u001b[39;00m\n\u001b[1;32m   1644\u001b[0m \n\u001b[1;32m   1645\u001b[0m \u001b[38;5;124;03m        See ``scoring`` parameter to know more about multiple metric\u001b[39;00m\n\u001b[1;32m   1646\u001b[0m \u001b[38;5;124;03m        evaluation.\u001b[39;00m\n\u001b[1;32m   1647\u001b[0m \n\u001b[1;32m   1648\u001b[0m \u001b[38;5;124;03m        .. versionchanged:: 0.20\u001b[39;00m\n\u001b[1;32m   1649\u001b[0m \u001b[38;5;124;03m            Support for callable added.\u001b[39;00m\n\u001b[1;32m   1650\u001b[0m \n\u001b[1;32m   1651\u001b[0m \u001b[38;5;124;03m    cv : int, cross-validation generator or an iterable, default=None\u001b[39;00m\n\u001b[1;32m   1652\u001b[0m \u001b[38;5;124;03m        Determines the cross-validation splitting strategy.\u001b[39;00m\n\u001b[1;32m   1653\u001b[0m \u001b[38;5;124;03m        Possible inputs for cv are:\u001b[39;00m\n\u001b[1;32m   1654\u001b[0m \n\u001b[1;32m   1655\u001b[0m \u001b[38;5;124;03m        - None, to use the default 5-fold cross validation,\u001b[39;00m\n\u001b[1;32m   1656\u001b[0m \u001b[38;5;124;03m        - integer, to specify the number of folds in a `(Stratified)KFold`,\u001b[39;00m\n\u001b[1;32m   1657\u001b[0m \u001b[38;5;124;03m        - :term:`CV splitter`,\u001b[39;00m\n\u001b[1;32m   1658\u001b[0m \u001b[38;5;124;03m        - An iterable yielding (train, test) splits as arrays of indices.\u001b[39;00m\n\u001b[1;32m   1659\u001b[0m \n\u001b[1;32m   1660\u001b[0m \u001b[38;5;124;03m        For integer/None inputs, if the estimator is a classifier and ``y`` is\u001b[39;00m\n\u001b[1;32m   1661\u001b[0m \u001b[38;5;124;03m        either binary or multiclass, :class:`StratifiedKFold` is used. In all\u001b[39;00m\n\u001b[1;32m   1662\u001b[0m \u001b[38;5;124;03m        other cases, :class:`KFold` is used. These splitters are instantiated\u001b[39;00m\n\u001b[1;32m   1663\u001b[0m \u001b[38;5;124;03m        with `shuffle=False` so the splits will be the same across calls.\u001b[39;00m\n\u001b[1;32m   1664\u001b[0m \n\u001b[1;32m   1665\u001b[0m \u001b[38;5;124;03m        Refer :ref:`User Guide <cross_validation>` for the various\u001b[39;00m\n\u001b[1;32m   1666\u001b[0m \u001b[38;5;124;03m        cross-validation strategies that can be used here.\u001b[39;00m\n\u001b[1;32m   1667\u001b[0m \n\u001b[1;32m   1668\u001b[0m \u001b[38;5;124;03m        .. versionchanged:: 0.22\u001b[39;00m\n\u001b[1;32m   1669\u001b[0m \u001b[38;5;124;03m            ``cv`` default value if None changed from 3-fold to 5-fold.\u001b[39;00m\n\u001b[1;32m   1670\u001b[0m \n\u001b[1;32m   1671\u001b[0m \u001b[38;5;124;03m    verbose : int\u001b[39;00m\n\u001b[1;32m   1672\u001b[0m \u001b[38;5;124;03m        Controls the verbosity: the higher, the more messages.\u001b[39;00m\n\u001b[1;32m   1673\u001b[0m \n\u001b[1;32m   1674\u001b[0m \u001b[38;5;124;03m        - >1 : the computation time for each fold and parameter candidate is\u001b[39;00m\n\u001b[1;32m   1675\u001b[0m \u001b[38;5;124;03m          displayed;\u001b[39;00m\n\u001b[1;32m   1676\u001b[0m \u001b[38;5;124;03m        - >2 : the score is also displayed;\u001b[39;00m\n\u001b[1;32m   1677\u001b[0m \u001b[38;5;124;03m        - >3 : the fold and candidate parameter indexes are also displayed\u001b[39;00m\n\u001b[1;32m   1678\u001b[0m \u001b[38;5;124;03m          together with the starting time of the computation.\u001b[39;00m\n\u001b[1;32m   1679\u001b[0m \n\u001b[1;32m   1680\u001b[0m \u001b[38;5;124;03m    pre_dispatch : int, or str, default='2*n_jobs'\u001b[39;00m\n\u001b[1;32m   1681\u001b[0m \u001b[38;5;124;03m        Controls the number of jobs that get dispatched during parallel\u001b[39;00m\n\u001b[1;32m   1682\u001b[0m \u001b[38;5;124;03m        execution. Reducing this number can be useful to avoid an\u001b[39;00m\n\u001b[1;32m   1683\u001b[0m \u001b[38;5;124;03m        explosion of memory consumption when more jobs get dispatched\u001b[39;00m\n\u001b[1;32m   1684\u001b[0m \u001b[38;5;124;03m        than CPUs can process. This parameter can be:\u001b[39;00m\n\u001b[1;32m   1685\u001b[0m \n\u001b[1;32m   1686\u001b[0m \u001b[38;5;124;03m            - None, in which case all the jobs are immediately\u001b[39;00m\n\u001b[1;32m   1687\u001b[0m \u001b[38;5;124;03m              created and spawned. Use this for lightweight and\u001b[39;00m\n\u001b[1;32m   1688\u001b[0m \u001b[38;5;124;03m              fast-running jobs, to avoid delays due to on-demand\u001b[39;00m\n\u001b[1;32m   1689\u001b[0m \u001b[38;5;124;03m              spawning of the jobs\u001b[39;00m\n\u001b[1;32m   1690\u001b[0m \n\u001b[1;32m   1691\u001b[0m \u001b[38;5;124;03m            - An int, giving the exact number of total jobs that are\u001b[39;00m\n\u001b[1;32m   1692\u001b[0m \u001b[38;5;124;03m              spawned\u001b[39;00m\n\u001b[1;32m   1693\u001b[0m \n\u001b[1;32m   1694\u001b[0m \u001b[38;5;124;03m            - A str, giving an expression as a function of n_jobs,\u001b[39;00m\n\u001b[1;32m   1695\u001b[0m \u001b[38;5;124;03m              as in '2*n_jobs'\u001b[39;00m\n\u001b[1;32m   1696\u001b[0m \n\u001b[1;32m   1697\u001b[0m \u001b[38;5;124;03m    random_state : int, RandomState instance or None, default=None\u001b[39;00m\n\u001b[1;32m   1698\u001b[0m \u001b[38;5;124;03m        Pseudo random number generator state used for random uniform sampling\u001b[39;00m\n\u001b[1;32m   1699\u001b[0m \u001b[38;5;124;03m        from lists of possible values instead of scipy.stats distributions.\u001b[39;00m\n\u001b[1;32m   1700\u001b[0m \u001b[38;5;124;03m        Pass an int for reproducible output across multiple\u001b[39;00m\n\u001b[1;32m   1701\u001b[0m \u001b[38;5;124;03m        function calls.\u001b[39;00m\n\u001b[1;32m   1702\u001b[0m \u001b[38;5;124;03m        See :term:`Glossary <random_state>`.\u001b[39;00m\n\u001b[1;32m   1703\u001b[0m \n\u001b[1;32m   1704\u001b[0m \u001b[38;5;124;03m    error_score : 'raise' or numeric, default=np.nan\u001b[39;00m\n\u001b[1;32m   1705\u001b[0m \u001b[38;5;124;03m        Value to assign to the score if an error occurs in estimator fitting.\u001b[39;00m\n\u001b[1;32m   1706\u001b[0m \u001b[38;5;124;03m        If set to 'raise', the error is raised. If a numeric value is given,\u001b[39;00m\n\u001b[1;32m   1707\u001b[0m \u001b[38;5;124;03m        FitFailedWarning is raised. This parameter does not affect the refit\u001b[39;00m\n\u001b[1;32m   1708\u001b[0m \u001b[38;5;124;03m        step, which will always raise the error.\u001b[39;00m\n\u001b[1;32m   1709\u001b[0m \n\u001b[1;32m   1710\u001b[0m \u001b[38;5;124;03m    return_train_score : bool, default=False\u001b[39;00m\n\u001b[1;32m   1711\u001b[0m \u001b[38;5;124;03m        If ``False``, the ``cv_results_`` attribute will not include training\u001b[39;00m\n\u001b[1;32m   1712\u001b[0m \u001b[38;5;124;03m        scores.\u001b[39;00m\n\u001b[1;32m   1713\u001b[0m \u001b[38;5;124;03m        Computing training scores is used to get insights on how different\u001b[39;00m\n\u001b[1;32m   1714\u001b[0m \u001b[38;5;124;03m        parameter settings impact the overfitting/underfitting trade-off.\u001b[39;00m\n\u001b[1;32m   1715\u001b[0m \u001b[38;5;124;03m        However computing the scores on the training set can be computationally\u001b[39;00m\n\u001b[1;32m   1716\u001b[0m \u001b[38;5;124;03m        expensive and is not strictly required to select the parameters that\u001b[39;00m\n\u001b[1;32m   1717\u001b[0m \u001b[38;5;124;03m        yield the best generalization performance.\u001b[39;00m\n\u001b[1;32m   1718\u001b[0m \n\u001b[1;32m   1719\u001b[0m \u001b[38;5;124;03m        .. versionadded:: 0.19\u001b[39;00m\n\u001b[1;32m   1720\u001b[0m \n\u001b[1;32m   1721\u001b[0m \u001b[38;5;124;03m        .. versionchanged:: 0.21\u001b[39;00m\n\u001b[1;32m   1722\u001b[0m \u001b[38;5;124;03m            Default value was changed from ``True`` to ``False``\u001b[39;00m\n\u001b[1;32m   1723\u001b[0m \n\u001b[1;32m   1724\u001b[0m \u001b[38;5;124;03m    Attributes\u001b[39;00m\n\u001b[1;32m   1725\u001b[0m \u001b[38;5;124;03m    ----------\u001b[39;00m\n\u001b[1;32m   1726\u001b[0m \u001b[38;5;124;03m    cv_results_ : dict of numpy (masked) ndarrays\u001b[39;00m\n\u001b[1;32m   1727\u001b[0m \u001b[38;5;124;03m        A dict with keys as column headers and values as columns, that can be\u001b[39;00m\n\u001b[1;32m   1728\u001b[0m \u001b[38;5;124;03m        imported into a pandas ``DataFrame``.\u001b[39;00m\n\u001b[1;32m   1729\u001b[0m \n\u001b[1;32m   1730\u001b[0m \u001b[38;5;124;03m        For instance the below given table\u001b[39;00m\n\u001b[1;32m   1731\u001b[0m \n\u001b[1;32m   1732\u001b[0m \u001b[38;5;124;03m        +--------------+-------------+-------------------+---+---------------+\u001b[39;00m\n\u001b[1;32m   1733\u001b[0m \u001b[38;5;124;03m        | param_kernel | param_gamma | split0_test_score |...|rank_test_score|\u001b[39;00m\n\u001b[1;32m   1734\u001b[0m \u001b[38;5;124;03m        +==============+=============+===================+===+===============+\u001b[39;00m\n\u001b[1;32m   1735\u001b[0m \u001b[38;5;124;03m        |    'rbf'     |     0.1     |       0.80        |...|       1       |\u001b[39;00m\n\u001b[1;32m   1736\u001b[0m \u001b[38;5;124;03m        +--------------+-------------+-------------------+---+---------------+\u001b[39;00m\n\u001b[1;32m   1737\u001b[0m \u001b[38;5;124;03m        |    'rbf'     |     0.2     |       0.84        |...|       3       |\u001b[39;00m\n\u001b[1;32m   1738\u001b[0m \u001b[38;5;124;03m        +--------------+-------------+-------------------+---+---------------+\u001b[39;00m\n\u001b[1;32m   1739\u001b[0m \u001b[38;5;124;03m        |    'rbf'     |     0.3     |       0.70        |...|       2       |\u001b[39;00m\n\u001b[1;32m   1740\u001b[0m \u001b[38;5;124;03m        +--------------+-------------+-------------------+---+---------------+\u001b[39;00m\n\u001b[1;32m   1741\u001b[0m \n\u001b[1;32m   1742\u001b[0m \u001b[38;5;124;03m        will be represented by a ``cv_results_`` dict of::\u001b[39;00m\n\u001b[1;32m   1743\u001b[0m \n\u001b[1;32m   1744\u001b[0m \u001b[38;5;124;03m            {\u001b[39;00m\n\u001b[1;32m   1745\u001b[0m \u001b[38;5;124;03m            'param_kernel' : masked_array(data = ['rbf', 'rbf', 'rbf'],\u001b[39;00m\n\u001b[1;32m   1746\u001b[0m \u001b[38;5;124;03m                                          mask = False),\u001b[39;00m\n\u001b[1;32m   1747\u001b[0m \u001b[38;5;124;03m            'param_gamma'  : masked_array(data = [0.1 0.2 0.3], mask = False),\u001b[39;00m\n\u001b[1;32m   1748\u001b[0m \u001b[38;5;124;03m            'split0_test_score'  : [0.80, 0.84, 0.70],\u001b[39;00m\n\u001b[1;32m   1749\u001b[0m \u001b[38;5;124;03m            'split1_test_score'  : [0.82, 0.50, 0.70],\u001b[39;00m\n\u001b[1;32m   1750\u001b[0m \u001b[38;5;124;03m            'mean_test_score'    : [0.81, 0.67, 0.70],\u001b[39;00m\n\u001b[1;32m   1751\u001b[0m \u001b[38;5;124;03m            'std_test_score'     : [0.01, 0.24, 0.00],\u001b[39;00m\n\u001b[1;32m   1752\u001b[0m \u001b[38;5;124;03m            'rank_test_score'    : [1, 3, 2],\u001b[39;00m\n\u001b[1;32m   1753\u001b[0m \u001b[38;5;124;03m            'split0_train_score' : [0.80, 0.92, 0.70],\u001b[39;00m\n\u001b[1;32m   1754\u001b[0m \u001b[38;5;124;03m            'split1_train_score' : [0.82, 0.55, 0.70],\u001b[39;00m\n\u001b[1;32m   1755\u001b[0m \u001b[38;5;124;03m            'mean_train_score'   : [0.81, 0.74, 0.70],\u001b[39;00m\n\u001b[1;32m   1756\u001b[0m \u001b[38;5;124;03m            'std_train_score'    : [0.01, 0.19, 0.00],\u001b[39;00m\n\u001b[1;32m   1757\u001b[0m \u001b[38;5;124;03m            'mean_fit_time'      : [0.73, 0.63, 0.43],\u001b[39;00m\n\u001b[1;32m   1758\u001b[0m \u001b[38;5;124;03m            'std_fit_time'       : [0.01, 0.02, 0.01],\u001b[39;00m\n\u001b[1;32m   1759\u001b[0m \u001b[38;5;124;03m            'mean_score_time'    : [0.01, 0.06, 0.04],\u001b[39;00m\n\u001b[1;32m   1760\u001b[0m \u001b[38;5;124;03m            'std_score_time'     : [0.00, 0.00, 0.00],\u001b[39;00m\n\u001b[1;32m   1761\u001b[0m \u001b[38;5;124;03m            'params'             : [{'kernel' : 'rbf', 'gamma' : 0.1}, ...],\u001b[39;00m\n\u001b[1;32m   1762\u001b[0m \u001b[38;5;124;03m            }\u001b[39;00m\n\u001b[1;32m   1763\u001b[0m \n\u001b[1;32m   1764\u001b[0m \u001b[38;5;124;03m        NOTE\u001b[39;00m\n\u001b[1;32m   1765\u001b[0m \n\u001b[1;32m   1766\u001b[0m \u001b[38;5;124;03m        The key ``'params'`` is used to store a list of parameter\u001b[39;00m\n\u001b[1;32m   1767\u001b[0m \u001b[38;5;124;03m        settings dicts for all the parameter candidates.\u001b[39;00m\n\u001b[1;32m   1768\u001b[0m \n\u001b[1;32m   1769\u001b[0m \u001b[38;5;124;03m        The ``mean_fit_time``, ``std_fit_time``, ``mean_score_time`` and\u001b[39;00m\n\u001b[1;32m   1770\u001b[0m \u001b[38;5;124;03m        ``std_score_time`` are all in seconds.\u001b[39;00m\n\u001b[1;32m   1771\u001b[0m \n\u001b[1;32m   1772\u001b[0m \u001b[38;5;124;03m        For multi-metric evaluation, the scores for all the scorers are\u001b[39;00m\n\u001b[1;32m   1773\u001b[0m \u001b[38;5;124;03m        available in the ``cv_results_`` dict at the keys ending with that\u001b[39;00m\n\u001b[1;32m   1774\u001b[0m \u001b[38;5;124;03m        scorer's name (``'_<scorer_name>'``) instead of ``'_score'`` shown\u001b[39;00m\n\u001b[1;32m   1775\u001b[0m \u001b[38;5;124;03m        above. ('split0_test_precision', 'mean_train_precision' etc.)\u001b[39;00m\n\u001b[1;32m   1776\u001b[0m \n\u001b[1;32m   1777\u001b[0m \u001b[38;5;124;03m    best_estimator_ : estimator\u001b[39;00m\n\u001b[1;32m   1778\u001b[0m \u001b[38;5;124;03m        Estimator that was chosen by the search, i.e. estimator\u001b[39;00m\n\u001b[1;32m   1779\u001b[0m \u001b[38;5;124;03m        which gave highest score (or smallest loss if specified)\u001b[39;00m\n\u001b[1;32m   1780\u001b[0m \u001b[38;5;124;03m        on the left out data. Not available if ``refit=False``.\u001b[39;00m\n\u001b[1;32m   1781\u001b[0m \n\u001b[1;32m   1782\u001b[0m \u001b[38;5;124;03m        For multi-metric evaluation, this attribute is present only if\u001b[39;00m\n\u001b[1;32m   1783\u001b[0m \u001b[38;5;124;03m        ``refit`` is specified.\u001b[39;00m\n\u001b[1;32m   1784\u001b[0m \n\u001b[1;32m   1785\u001b[0m \u001b[38;5;124;03m        See ``refit`` parameter for more information on allowed values.\u001b[39;00m\n\u001b[1;32m   1786\u001b[0m \n\u001b[1;32m   1787\u001b[0m \u001b[38;5;124;03m    best_score_ : float\u001b[39;00m\n\u001b[1;32m   1788\u001b[0m \u001b[38;5;124;03m        Mean cross-validated score of the best_estimator.\u001b[39;00m\n\u001b[1;32m   1789\u001b[0m \n\u001b[1;32m   1790\u001b[0m \u001b[38;5;124;03m        For multi-metric evaluation, this is not available if ``refit`` is\u001b[39;00m\n\u001b[1;32m   1791\u001b[0m \u001b[38;5;124;03m        ``False``. See ``refit`` parameter for more information.\u001b[39;00m\n\u001b[1;32m   1792\u001b[0m \n\u001b[1;32m   1793\u001b[0m \u001b[38;5;124;03m        This attribute is not available if ``refit`` is a function.\u001b[39;00m\n\u001b[1;32m   1794\u001b[0m \n\u001b[1;32m   1795\u001b[0m \u001b[38;5;124;03m    best_params_ : dict\u001b[39;00m\n\u001b[1;32m   1796\u001b[0m \u001b[38;5;124;03m        Parameter setting that gave the best results on the hold out data.\u001b[39;00m\n\u001b[1;32m   1797\u001b[0m \n\u001b[1;32m   1798\u001b[0m \u001b[38;5;124;03m        For multi-metric evaluation, this is not available if ``refit`` is\u001b[39;00m\n\u001b[1;32m   1799\u001b[0m \u001b[38;5;124;03m        ``False``. See ``refit`` parameter for more information.\u001b[39;00m\n\u001b[1;32m   1800\u001b[0m \n\u001b[1;32m   1801\u001b[0m \u001b[38;5;124;03m    best_index_ : int\u001b[39;00m\n\u001b[1;32m   1802\u001b[0m \u001b[38;5;124;03m        The index (of the ``cv_results_`` arrays) which corresponds to the best\u001b[39;00m\n\u001b[1;32m   1803\u001b[0m \u001b[38;5;124;03m        candidate parameter setting.\u001b[39;00m\n\u001b[1;32m   1804\u001b[0m \n\u001b[1;32m   1805\u001b[0m \u001b[38;5;124;03m        The dict at ``search.cv_results_['params'][search.best_index_]`` gives\u001b[39;00m\n\u001b[1;32m   1806\u001b[0m \u001b[38;5;124;03m        the parameter setting for the best model, that gives the highest\u001b[39;00m\n\u001b[1;32m   1807\u001b[0m \u001b[38;5;124;03m        mean score (``search.best_score_``).\u001b[39;00m\n\u001b[1;32m   1808\u001b[0m \n\u001b[1;32m   1809\u001b[0m \u001b[38;5;124;03m        For multi-metric evaluation, this is not available if ``refit`` is\u001b[39;00m\n\u001b[1;32m   1810\u001b[0m \u001b[38;5;124;03m        ``False``. See ``refit`` parameter for more information.\u001b[39;00m\n\u001b[1;32m   1811\u001b[0m \n\u001b[1;32m   1812\u001b[0m \u001b[38;5;124;03m    scorer_ : function or a dict\u001b[39;00m\n\u001b[1;32m   1813\u001b[0m \u001b[38;5;124;03m        Scorer function used on the held out data to choose the best\u001b[39;00m\n\u001b[1;32m   1814\u001b[0m \u001b[38;5;124;03m        parameters for the model.\u001b[39;00m\n\u001b[1;32m   1815\u001b[0m \n\u001b[1;32m   1816\u001b[0m \u001b[38;5;124;03m        For multi-metric evaluation, this attribute holds the validated\u001b[39;00m\n\u001b[1;32m   1817\u001b[0m \u001b[38;5;124;03m        ``scoring`` dict which maps the scorer key to the scorer callable.\u001b[39;00m\n\u001b[1;32m   1818\u001b[0m \n\u001b[1;32m   1819\u001b[0m \u001b[38;5;124;03m    n_splits_ : int\u001b[39;00m\n\u001b[1;32m   1820\u001b[0m \u001b[38;5;124;03m        The number of cross-validation splits (folds/iterations).\u001b[39;00m\n\u001b[1;32m   1821\u001b[0m \n\u001b[1;32m   1822\u001b[0m \u001b[38;5;124;03m    refit_time_ : float\u001b[39;00m\n\u001b[1;32m   1823\u001b[0m \u001b[38;5;124;03m        Seconds used for refitting the best model on the whole dataset.\u001b[39;00m\n\u001b[1;32m   1824\u001b[0m \n\u001b[1;32m   1825\u001b[0m \u001b[38;5;124;03m        This is present only if ``refit`` is not False.\u001b[39;00m\n\u001b[1;32m   1826\u001b[0m \n\u001b[1;32m   1827\u001b[0m \u001b[38;5;124;03m        .. versionadded:: 0.20\u001b[39;00m\n\u001b[1;32m   1828\u001b[0m \n\u001b[1;32m   1829\u001b[0m \u001b[38;5;124;03m    multimetric_ : bool\u001b[39;00m\n\u001b[1;32m   1830\u001b[0m \u001b[38;5;124;03m        Whether or not the scorers compute several metrics.\u001b[39;00m\n\u001b[1;32m   1831\u001b[0m \n\u001b[1;32m   1832\u001b[0m \u001b[38;5;124;03m    classes_ : ndarray of shape (n_classes,)\u001b[39;00m\n\u001b[1;32m   1833\u001b[0m \u001b[38;5;124;03m        The classes labels. This is present only if ``refit`` is specified and\u001b[39;00m\n\u001b[1;32m   1834\u001b[0m \u001b[38;5;124;03m        the underlying estimator is a classifier.\u001b[39;00m\n\u001b[1;32m   1835\u001b[0m \n\u001b[1;32m   1836\u001b[0m \u001b[38;5;124;03m    n_features_in_ : int\u001b[39;00m\n\u001b[1;32m   1837\u001b[0m \u001b[38;5;124;03m        Number of features seen during :term:`fit`. Only defined if\u001b[39;00m\n\u001b[1;32m   1838\u001b[0m \u001b[38;5;124;03m        `best_estimator_` is defined (see the documentation for the `refit`\u001b[39;00m\n\u001b[1;32m   1839\u001b[0m \u001b[38;5;124;03m        parameter for more details) and that `best_estimator_` exposes\u001b[39;00m\n\u001b[1;32m   1840\u001b[0m \u001b[38;5;124;03m        `n_features_in_` when fit.\u001b[39;00m\n\u001b[1;32m   1841\u001b[0m \n\u001b[1;32m   1842\u001b[0m \u001b[38;5;124;03m        .. versionadded:: 0.24\u001b[39;00m\n\u001b[1;32m   1843\u001b[0m \n\u001b[1;32m   1844\u001b[0m \u001b[38;5;124;03m    feature_names_in_ : ndarray of shape (`n_features_in_`,)\u001b[39;00m\n\u001b[1;32m   1845\u001b[0m \u001b[38;5;124;03m        Names of features seen during :term:`fit`. Only defined if\u001b[39;00m\n\u001b[1;32m   1846\u001b[0m \u001b[38;5;124;03m        `best_estimator_` is defined (see the documentation for the `refit`\u001b[39;00m\n\u001b[1;32m   1847\u001b[0m \u001b[38;5;124;03m        parameter for more details) and that `best_estimator_` exposes\u001b[39;00m\n\u001b[1;32m   1848\u001b[0m \u001b[38;5;124;03m        `feature_names_in_` when fit.\u001b[39;00m\n\u001b[1;32m   1849\u001b[0m \n\u001b[1;32m   1850\u001b[0m \u001b[38;5;124;03m        .. versionadded:: 1.0\u001b[39;00m\n\u001b[1;32m   1851\u001b[0m \n\u001b[1;32m   1852\u001b[0m \u001b[38;5;124;03m    See Also\u001b[39;00m\n\u001b[1;32m   1853\u001b[0m \u001b[38;5;124;03m    --------\u001b[39;00m\n\u001b[1;32m   1854\u001b[0m \u001b[38;5;124;03m    GridSearchCV : Does exhaustive search over a grid of parameters.\u001b[39;00m\n\u001b[1;32m   1855\u001b[0m \u001b[38;5;124;03m    ParameterSampler : A generator over parameter settings, constructed from\u001b[39;00m\n\u001b[1;32m   1856\u001b[0m \u001b[38;5;124;03m        param_distributions.\u001b[39;00m\n\u001b[1;32m   1857\u001b[0m \n\u001b[1;32m   1858\u001b[0m \u001b[38;5;124;03m    Notes\u001b[39;00m\n\u001b[1;32m   1859\u001b[0m \u001b[38;5;124;03m    -----\u001b[39;00m\n\u001b[1;32m   1860\u001b[0m \u001b[38;5;124;03m    The parameters selected are those that maximize the score of the held-out\u001b[39;00m\n\u001b[1;32m   1861\u001b[0m \u001b[38;5;124;03m    data, according to the scoring parameter.\u001b[39;00m\n\u001b[1;32m   1862\u001b[0m \n\u001b[1;32m   1863\u001b[0m \u001b[38;5;124;03m    If `n_jobs` was set to a value higher than one, the data is copied for each\u001b[39;00m\n\u001b[1;32m   1864\u001b[0m \u001b[38;5;124;03m    parameter setting(and not `n_jobs` times). This is done for efficiency\u001b[39;00m\n\u001b[1;32m   1865\u001b[0m \u001b[38;5;124;03m    reasons if individual jobs take very little time, but may raise errors if\u001b[39;00m\n\u001b[1;32m   1866\u001b[0m \u001b[38;5;124;03m    the dataset is large and not enough memory is available.  A workaround in\u001b[39;00m\n\u001b[1;32m   1867\u001b[0m \u001b[38;5;124;03m    this case is to set `pre_dispatch`. Then, the memory is copied only\u001b[39;00m\n\u001b[1;32m   1868\u001b[0m \u001b[38;5;124;03m    `pre_dispatch` many times. A reasonable value for `pre_dispatch` is `2 *\u001b[39;00m\n\u001b[1;32m   1869\u001b[0m \u001b[38;5;124;03m    n_jobs`.\u001b[39;00m\n\u001b[1;32m   1870\u001b[0m \n\u001b[1;32m   1871\u001b[0m \u001b[38;5;124;03m    Examples\u001b[39;00m\n\u001b[1;32m   1872\u001b[0m \u001b[38;5;124;03m    --------\u001b[39;00m\n\u001b[1;32m   1873\u001b[0m \u001b[38;5;124;03m    >>> from sklearn.datasets import load_iris\u001b[39;00m\n\u001b[1;32m   1874\u001b[0m \u001b[38;5;124;03m    >>> from sklearn.linear_model import LogisticRegression\u001b[39;00m\n\u001b[1;32m   1875\u001b[0m \u001b[38;5;124;03m    >>> from sklearn.model_selection import RandomizedSearchCV\u001b[39;00m\n\u001b[1;32m   1876\u001b[0m \u001b[38;5;124;03m    >>> from scipy.stats import uniform\u001b[39;00m\n\u001b[1;32m   1877\u001b[0m \u001b[38;5;124;03m    >>> iris = load_iris()\u001b[39;00m\n\u001b[1;32m   1878\u001b[0m \u001b[38;5;124;03m    >>> logistic = LogisticRegression(solver='saga', tol=1e-2, max_iter=200,\u001b[39;00m\n\u001b[1;32m   1879\u001b[0m \u001b[38;5;124;03m    ...                               random_state=0)\u001b[39;00m\n\u001b[1;32m   1880\u001b[0m \u001b[38;5;124;03m    >>> distributions = dict(C=uniform(loc=0, scale=4),\u001b[39;00m\n\u001b[1;32m   1881\u001b[0m \u001b[38;5;124;03m    ...                      penalty=['l2', 'l1'])\u001b[39;00m\n\u001b[1;32m   1882\u001b[0m \u001b[38;5;124;03m    >>> clf = RandomizedSearchCV(logistic, distributions, random_state=0)\u001b[39;00m\n\u001b[1;32m   1883\u001b[0m \u001b[38;5;124;03m    >>> search = clf.fit(iris.data, iris.target)\u001b[39;00m\n\u001b[1;32m   1884\u001b[0m \u001b[38;5;124;03m    >>> search.best_params_\u001b[39;00m\n\u001b[1;32m   1885\u001b[0m \u001b[38;5;124;03m    {'C': 2..., 'penalty': 'l1'}\u001b[39;00m\n\u001b[1;32m   1886\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m   1888\u001b[0m     _required_parameters \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mestimator\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparam_distributions\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m   1890\u001b[0m     _parameter_constraints: \u001b[38;5;28mdict\u001b[39m \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m   1891\u001b[0m         \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mBaseSearchCV\u001b[38;5;241m.\u001b[39m_parameter_constraints,\n\u001b[1;32m   1892\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparam_distributions\u001b[39m\u001b[38;5;124m\"\u001b[39m: [\u001b[38;5;28mdict\u001b[39m, \u001b[38;5;28mlist\u001b[39m],\n\u001b[1;32m   1893\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mn_iter\u001b[39m\u001b[38;5;124m\"\u001b[39m: [Interval(numbers\u001b[38;5;241m.\u001b[39mIntegral, \u001b[38;5;241m1\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m, closed\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mleft\u001b[39m\u001b[38;5;124m\"\u001b[39m)],\n\u001b[1;32m   1894\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrandom_state\u001b[39m\u001b[38;5;124m\"\u001b[39m: [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrandom_state\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[1;32m   1895\u001b[0m     }\n",
      "File \u001b[0;32m~/miniconda3/lib/python3.11/site-packages/sklearn/model_selection/_search.py:964\u001b[0m, in \u001b[0;36mevaluate_candidates\u001b[0;34m(candidate_params, cv, more_results)\u001b[0m\n\u001b[1;32m    959\u001b[0m         all_more_results[key]\u001b[38;5;241m.\u001b[39mextend(value)\n\u001b[1;32m    961\u001b[0m \u001b[38;5;28;01mnonlocal\u001b[39;00m results\n\u001b[1;32m    962\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_format_results(\n\u001b[1;32m    963\u001b[0m     all_candidate_params, n_splits, all_out, all_more_results\n\u001b[0;32m--> 964\u001b[0m )\n\u001b[1;32m    966\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m results\n",
      "File \u001b[0;32m~/miniconda3/lib/python3.11/site-packages/sklearn/utils/parallel.py:74\u001b[0m, in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m     71\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdelayed\u001b[39m(function):\n\u001b[1;32m     72\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Decorator used to capture the arguments of a function.\u001b[39;00m\n\u001b[1;32m     73\u001b[0m \n\u001b[0;32m---> 74\u001b[0m \u001b[38;5;124;03m    This alternative to `joblib.delayed` is meant to be used in conjunction\u001b[39;00m\n\u001b[1;32m     75\u001b[0m \u001b[38;5;124;03m    with `sklearn.utils.parallel.Parallel`. The latter captures the scikit-\u001b[39;00m\n\u001b[1;32m     76\u001b[0m \u001b[38;5;124;03m    learn configuration by calling `sklearn.get_config()` in the current\u001b[39;00m\n\u001b[1;32m     77\u001b[0m \u001b[38;5;124;03m    thread, prior to dispatching the first task. The captured configuration is\u001b[39;00m\n\u001b[1;32m     78\u001b[0m \u001b[38;5;124;03m    then propagated and enabled for the duration of the execution of the\u001b[39;00m\n\u001b[1;32m     79\u001b[0m \u001b[38;5;124;03m    delayed function in the joblib workers.\u001b[39;00m\n\u001b[1;32m     80\u001b[0m \n\u001b[1;32m     81\u001b[0m \u001b[38;5;124;03m    .. versionchanged:: 1.3\u001b[39;00m\n\u001b[1;32m     82\u001b[0m \u001b[38;5;124;03m       `delayed` was moved from `sklearn.utils.fixes` to `sklearn.utils.parallel`\u001b[39;00m\n\u001b[1;32m     83\u001b[0m \u001b[38;5;124;03m       in scikit-learn 1.3.\u001b[39;00m\n\u001b[1;32m     84\u001b[0m \n\u001b[1;32m     85\u001b[0m \u001b[38;5;124;03m    Parameters\u001b[39;00m\n\u001b[1;32m     86\u001b[0m \u001b[38;5;124;03m    ----------\u001b[39;00m\n\u001b[1;32m     87\u001b[0m \u001b[38;5;124;03m    function : callable\u001b[39;00m\n\u001b[1;32m     88\u001b[0m \u001b[38;5;124;03m        The function to be delayed.\u001b[39;00m\n\u001b[1;32m     89\u001b[0m \n\u001b[1;32m     90\u001b[0m \u001b[38;5;124;03m    Returns\u001b[39;00m\n\u001b[1;32m     91\u001b[0m \u001b[38;5;124;03m    -------\u001b[39;00m\n\u001b[1;32m     92\u001b[0m \u001b[38;5;124;03m    output: tuple\u001b[39;00m\n\u001b[1;32m     93\u001b[0m \u001b[38;5;124;03m        Tuple containing the delayed function, the positional arguments, and the\u001b[39;00m\n\u001b[1;32m     94\u001b[0m \u001b[38;5;124;03m        keyword arguments.\u001b[39;00m\n\u001b[1;32m     95\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m     97\u001b[0m     \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(function)\n\u001b[1;32m     98\u001b[0m     \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdelayed_function\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m     99\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m _FuncWrapper(function), args, kwargs\n",
      "File \u001b[0;32m~/miniconda3/lib/python3.11/site-packages/joblib/parallel.py:2007\u001b[0m, in \u001b[0;36mParallel.__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   2001\u001b[0m \u001b[38;5;66;03m# The first item from the output is blank, but it makes the interpreter\u001b[39;00m\n\u001b[1;32m   2002\u001b[0m \u001b[38;5;66;03m# progress until it enters the Try/Except block of the generator and\u001b[39;00m\n\u001b[1;32m   2003\u001b[0m \u001b[38;5;66;03m# reaches the first `yield` statement. This starts the asynchronous\u001b[39;00m\n\u001b[1;32m   2004\u001b[0m \u001b[38;5;66;03m# dispatch of the tasks to the workers.\u001b[39;00m\n\u001b[1;32m   2005\u001b[0m \u001b[38;5;28mnext\u001b[39m(output)\n\u001b[0;32m-> 2007\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m output \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mreturn_generator \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43moutput\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniconda3/lib/python3.11/site-packages/joblib/parallel.py:1650\u001b[0m, in \u001b[0;36mParallel._get_outputs\u001b[0;34m(self, iterator, pre_dispatch)\u001b[0m\n\u001b[1;32m   1647\u001b[0m     \u001b[38;5;28;01myield\u001b[39;00m\n\u001b[1;32m   1649\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backend\u001b[38;5;241m.\u001b[39mretrieval_context():\n\u001b[0;32m-> 1650\u001b[0m         \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_retrieve()\n\u001b[1;32m   1652\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mGeneratorExit\u001b[39;00m:\n\u001b[1;32m   1653\u001b[0m     \u001b[38;5;66;03m# The generator has been garbage collected before being fully\u001b[39;00m\n\u001b[1;32m   1654\u001b[0m     \u001b[38;5;66;03m# consumed. This aborts the remaining tasks if possible and warn\u001b[39;00m\n\u001b[1;32m   1655\u001b[0m     \u001b[38;5;66;03m# the user if necessary.\u001b[39;00m\n\u001b[1;32m   1656\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n",
      "File \u001b[0;32m~/miniconda3/lib/python3.11/site-packages/joblib/parallel.py:1762\u001b[0m, in \u001b[0;36mParallel._retrieve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1757\u001b[0m \u001b[38;5;66;03m# If the next job is not ready for retrieval yet, we just wait for\u001b[39;00m\n\u001b[1;32m   1758\u001b[0m \u001b[38;5;66;03m# async callbacks to progress.\u001b[39;00m\n\u001b[1;32m   1759\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ((\u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jobs) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m\n\u001b[1;32m   1760\u001b[0m     (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jobs[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mget_status(\n\u001b[1;32m   1761\u001b[0m         timeout\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtimeout) \u001b[38;5;241m==\u001b[39m TASK_PENDING)):\n\u001b[0;32m-> 1762\u001b[0m     \u001b[43mtime\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msleep\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0.01\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1763\u001b[0m     \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[1;32m   1765\u001b[0m \u001b[38;5;66;03m# We need to be careful: the job list can be filling up as\u001b[39;00m\n\u001b[1;32m   1766\u001b[0m \u001b[38;5;66;03m# we empty it and Python list are not thread-safe by\u001b[39;00m\n\u001b[1;32m   1767\u001b[0m \u001b[38;5;66;03m# default hence the use of the lock\u001b[39;00m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# 定义参数网格进行超参数调优\n",
    "param_grid = {\n",
    "    'classifier__n_estimators': [50, 100, 200],\n",
    "    'classifier__max_depth': [None, 10, 20, 30],\n",
    "    'classifier__min_samples_split': [2, 5, 10],\n",
    "    'classifier__min_samples_leaf': [1, 2, 4]\n",
    "}\n",
    "\n",
    "# 使用GridSearchCV进行超参数调优（监督学习算法）\n",
    "grid_search = GridSearchCV(estimator=pipeline, param_grid=param_grid, cv=5, n_jobs=-1, scoring='accuracy')\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "# 输出最佳参数和模型评估\n",
    "print(f\"最佳参数: {grid_search.best_params_}\")\n",
    "print(f\"最佳模型评估: {grid_search.best_score_}\")\n",
    "\n",
    "# 使用最佳模型进行预测和评估\n",
    "best_model = grid_search.best_estimator_\n",
    "print(\"最佳模型的测试集评估:\")\n",
    "print(classification_report(y_test, best_model.predict(X_test)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b476d919-5dd9-43e5-8f2a-88a294b2e94f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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